Multi-Omics Co-Regulation & Modularity Fidelity — calc_coregulation

Evaluates the preservation of co-regulated gene/feature modules between reference and simulated multi-omics datasets (Monzo et al., 2025; Arzalluz-Luque et al., 2022).

Usage

calc_coregulation_fidelity(
  ref_data,
  sim_data,
  modules,
  method = c("pearson", "spearman")
)

Arguments

ref_data: Reference feature-by-cell matrix or data frame.
sim_data: Simulated feature-by-cell matrix or data frame.
modules: A list of character vectors representing feature clusters/modules, or a named vector/factor of module assignments per feature.
method: Correlation method: "pearson" (default) or "spearman".

Value

A list containing module correlation r, RMSE, MAE, and modularity fidelity.

Examples

data(example_scrna, package = "scSimEval")
modules <- list(Module_1 = rownames(example_scrna$ref)[seq_len(30)],
                Module_2 = rownames(example_scrna$ref)[31:60])
calc_coregulation_fidelity(example_scrna$ref, example_scrna$sim, modules)
#> $module_correlation_r
#> [1] -0.002873385
#> 
#> $module_correlation_rmse
#> [1] 0.1573539
#> 
#> $module_correlation_mae
#> [1] 0.1248352
#> 
#> $ref_modularity_ratio
#> [1] -0.02873166
#> 
#> $sim_modularity_ratio
#> [1] -0.00725975
#> 
#> $modularity_fidelity
#> [1] 0
#>